---
title: 'Quickstart'
description: 'Get started with OpenLIT Operator in 5 minutes'
icon: "bolt"
---

import HelmRepoSetup from '/snippets/helm-repo-setup.mdx';
import PlatformInstall from '/snippets/openlit-platform-install.mdx';
import OperatorInstall from '/snippets/openlit-operator-install.mdx';

In this 5-minute tutorial, you'll deploy an example AI Agent in Kubernetes and automatically get complete observability with zero code changes. 

Using OpenLIT Operator, you'll capture distributed traces showing LLM costs, token usage, and agent performance metrics - then visualize everything in the OpenLIT dashboard.

## 📋 Prerequisites

- **Kubernetes cluster+** with cluster admin access
- **Helm** package manager
- **kubectl** configured for your cluster

<AccordionGroup>
  <Accordion title="☸️ Don't have a Kubernetes cluster? Create one locally">
    We recommend using K3d or minikube for trying OpenLIT Operator out in a local environment.

    <Tabs>
      <Tab title="k3d">
        1. [Install k3d](https://k3d.io/stable/#installation) following their official guide
        2. Create cluster:
        ```bash
        k3d cluster create openlit-demo
        ```
      </Tab>

      <Tab title="minikube">
        1. [Install minikube](https://minikube.sigs.k8s.io/docs/start/) following their official guide
        2. Start cluster:
        ```bash
        minikube start --cpus=2 --memory=4096mb --driver=docker
        ```
      </Tab>
    </Tabs>
  </Accordion>
</AccordionGroup>

<Steps titleSize="h3">
  <Step title="Deploy OpenLIT Platform">
    Install the OpenLIT observability platform to collect and monitor your LLM App and AI Agent performance:

    <HelmRepoSetup />

    <PlatformInstall />
  </Step>

  <Step title="Deploy OpenLIT Operator">
    Install the OpenLIT Operator to enable zero-code instrumentation:

    <OperatorInstall />

    **Verify the operator is running**

    ```bash
    # Check operator pod status
    kubectl get pods -n openlit -l app.kubernetes.io/name=openlit-operator
    ```

    Expected output:
    ```bash
    NAME                                READY   STATUS    RESTARTS   AGE
    openlit-operator-7b9c8d5f7b-xyz12   1/1     Running   0          30s
    ```
  </Step>

  <Step title="Create AutoInstrumentation Custom Resource">
    Create an AutoInstrumentation resource to define how your AI apps should be instrumented:

    ```bash
    kubectl apply -f - <<EOF
    apiVersion: openlit.io/v1alpha1
    kind: AutoInstrumentation
    metadata:
      name: quickstart-instrumentation
      namespace: default
    spec:
      selector:
        matchLabels:
          instrumentation: openlit
      python:
        instrumentation:
          enabled: true
      otlp:
        endpoint: "http://openlit.openlit.svc.cluster.local:4318"
    EOF
    ```

    <Warning>
    **Already have AI applications running?** You'll need to restart them to enable instrumentation:
    ```bash
    kubectl rollout restart deployment <your-deployment-name>
    ```
    </Warning>
  </Step>

  <Step title="Deploy the example AI Agent">
    Deploy the example AI agent built using CrewAI:

    ```bash
    kubectl apply -f https://raw.githubusercontent.com/openlit/openlit/main/operator/examples/test-application-deployment.yaml
    ```

  </Step>

  <Step title="View Traces and metrics in OpenLIT">
    Access the OpenLIT dashboard to view your AI application traces:

    **Port Forward to OpenLIT**

    ```bash
    # Forward the OpenLIT dashboard port
    kubectl port-forward -n openlit svc/openlit 3000:3000
    ```

    **Access Dashboard**

    1. Open your browser and navigate to `http://localhost:3000`
    2. Navigate to **Traces** section in the dashboard

    **What You'll See**

    In the OpenLIT dashboard, you'll see:

    - **Service Overview**: Your `openlit-test-app` service with health metrics
    - **Trace Timeline**: Individual traces for HTTP requests and OpenAI API calls
    - **LLM Operations**: Detailed spans showing OpenAI API calls with token usage
    - **Performance Metrics**: Response times, error rates, and throughput
    - **Cost Tracking**: Token usage and estimated costs

  </Step>
</Steps>
